If it's a target, it's a pan-cancer target: Tissue is not the issue.

Cancer is traditionally diagnosed and treated on the basis of its organ of origin (e.g., lung or colon cancer). However, organ-of-origin diagnostics does not reveal the underlying oncogenic drivers. Fortunately, molecular diagnostics have advanced at a breathtaking pace, and it is increasingly apparent that cancer is a disease of the genome. Hence, we now have multiple genomic biomarker-based, tissue-agnostic Food and Drug Administration approvals for both gene- and immune-targeted therapies (larotrectinib/entrectinib, for NTRK fusions; selpercatinib, RET fusions; dabrafenib plus trametinib, BRAFV600E mutations; pembrolizumab/dostarlimab, microsatellite instability; and pembrolizumab for high tumor mutational burden; pemigatinib is also approved for FGFR1-rearranged myeloid/lymphoid neoplasms). There are emerging targets as well, including but not limited to ALK, BRCA and/or homologous repair deficiency, ERBB2 (HER2), IDH1/2, KIT, KRASG12C, NRG1, and VHL. Many tissue-agnostic approvals center on rare/ultra-rare biomarkers (often < 1 % of cancers), necessitating screening hundreds of tumors to find a single one harboring the cognate molecular alteration. Approval has generally been based on small single-arm studies (<30-100 patients) with high response rates (>30 % to > 75 %) of remarkable durability. Because of biomarker rarity, single-gene testing is not practical; next generation sequencing of hundreds of genes must be performed to obtain timely answers. Resistance to biomarker-driven therapeutics is often due to secondary mutations or co-driver gene defects; studies are now addressing the need for customized drug combinations matched to the complex molecular alteration portfolio in each tumor. Future investigation should expand tissue-agnostic therapeutics to encompass both hematologic and solid malignancies and include biomarkers beyond those that are DNA-based.

KIT ATP-Binding Pocket/Activation Loop Mutations in GI Stromal Tumor: Emerging Mechanisms of Kinase Inhibitor Escape.

PURPOSE: Imatinib resistance in GI stromal tumors (GISTs) is primarily caused by secondary KIT mutations, and clonal heterogeneity of these secondary mutations represents a major treatment obstacle. KIT inhibitors used after imatinib have clinical activity, albeit with limited benefit. Ripretinib is a potent inhibitor of secondary KIT mutations in the activation loop (AL). However, clinical benefit in fourth line remains limited and the molecular mechanisms of ripretinib resistance are largely unknown. PATIENTS AND METHODS: Progressing lesions of 25 patients with GISTs refractory to ripretinib were sequenced for KIT resistance mutations. Resistant genotypes were validated and characterized using novel cell line models and in silico modeling. RESULTS: GISTs progressing on ripretinib were enriched for secondary mutations in the ATP-binding pocket (AP), which frequently occur in cis with preexisting AL mutations, resulting in highly resistant AP/AL genotypes. AP/AL mutations were rarely observed in a cohort of progressing GIST samples from the preripretinib era but represented 50% of secondary KIT mutations in patients with tumors resistant to ripretinib. In GIST cell lines harboring secondary KIT AL mutations, the sole genomic escape mechanisms during ripretinib drug selection were AP/AL mutations. Ripretinib and sunitinib synergize against mixed clones with secondary AP or AL mutants but do not suppress clones with AP/AL genotypes. CONCLUSION: Our findings underscore that KIT remains the central oncogenic driver even in late lines of GIST therapy. KIT-inhibitor combinations may suppress resistance because of secondary KIT mutations. However, the emergence of KIT AP/AL mutations after ripretinib treatment calls for new strategies in the development of next-generation KIT inhibitors.

Transforming the Future of Surgeon-Scientists.

OBJECTIVE: To create a blueprint for surgical department leaders, academic institutions, and funding agencies to optimally support surgeon-scientists. BACKGROUND: Scientific contributions by surgeons have been transformative across many medical disciplines. Surgeon-scientists provide a distinct approach and mindset toward key scientific questions. However, lack of institutional support, pressure for increased clinical productivity, and growing administrative burden are major challenges for the surgeon-scientist, as is the time-consuming nature of surgical training and practice. METHODS: An American Surgical Association Research Sustainability Task Force was created to outline a blueprint for sustainable science in surgery. Leaders from top NIH-sponsored departments of surgery engaged in video and in-person meetings between January and April 2023. A strength, weakness, opportunities, threats analysis was performed, and workgroups focused on the roles of surgeons, the department and institutions, and funding agencies. RESULTS: Taskforce recommendations: (1) SURGEONS: Growth mindset : identifying research focus, long-term planning, patience/tenacity, team science, collaborations with disparate experts; Skill set : align skills and research, fill critical skill gaps, develop team leadership skills; DEPARTMENT OF SURGERY (DOS): (2) MENTORSHIP: Chair : mentor-mentee matching/regular meetings/accountability, review of junior faculty progress, mentorship training requirement, recognition of mentorship (eg, relative value unit equivalent, awards; Mentor: dedicated time, relevant scientific expertise, extramural funding, experience and/or trained as mentor, trusted advisor; Mentee : enthusiastic/eager, proactive, open to feedback, clear about goals; (3) FINANCIAL SUSTAINABILITY: diversification of research portfolio, identification of matching funding sources, departmental resource awards (eg, T-/P-grants), leveraging of institutional resources, negotiation of formalized/formulaic funds flow investment from academic medical center toward science, philanthropy; (4) STRUCTURAL/STRATEGIC SUPPORT: Structural: grants administrative support, biostats/bioinformatics support, clinical trial and research support, regulatory support, shared departmental laboratory space/equipment; Strategic: hiring diverse surgeon-scientist/scientists faculty across DOS, strategic faculty retention/ recruitment, philanthropy, career development support, progress tracking, grant writing support, DOS-wide research meetings, regular DOS strategic research planning; (5) COMMUNITY AND CULTURE: Community: right mix of faculty, connection surgeon with broad scientific community; Culture: building research infrastructure, financial support for research, projecting importance of research (awards, grand rounds, shoutouts); (6) THE ROLE OF INSTITUTIONS: Foundation: research space co-location, flexible start-up packages, courses/mock study section, awards, diverse institutional mentorship teams; Nurture: institutional infrastructure, funding (eg, endowed chairs), promotion friendly toward surgeon-scientists, surgeon-scientists in institutional leadership positions; Expectations: RVU target relief, salary gap funding, competitive starting salaries, longitudinal salary strategy; (7) THE ROLE OF FUNDING AGENCIES: change surgeon research training paradigm, offer alternate awards to K-awards, increasing salary cap to reflect market reality, time extension for surgeon early-stage investigator status, surgeon representation on study section, focused award strategies for professional societies/foundations. CONCLUSIONS: Authentic recommitment from surgeon leaders with intentional and ambitious actions from institutions, corporations, funders, and society is essential in order to reap the essential benefits of surgeon-scientists toward advancements of science.

At the right dose: personalised (N-of-1) dosing for precision oncology.

The objective of oncology therapeutics, especially in the age of precision medicine, is to give the right drug(s) to the right patient at the right time. Yet, a major challenge is finding the right dose for each patient. Determining safe and efficacious doses of oncology treatments, especially for novel combination therapies, can be challenging. Moreover, traditionally, dosing cancer drugs is based on giving each patient the same dose (a flat dose) or a dose based on surface area/weight. But patients' ability to tolerate drugs is influenced by additional factors including, but not limited to age, gender, race, comorbidities, organ function, and metabolism. Herein, we present evidence that, in the era of targeted drugs, individualised drug dosing determined by starting at reduced doses and using intrapatient dose escalation can yield safe and effective personalised dosing of novel combinations of approved drugs that have not previously undergone formal phase I trials and can also optimise dosing of tested drug regimens.

Challenges and prospects of CSF1R targeting for advanced malignancies.

CSF1R expression modulates tumor-associated macrophages, making CSF1R blockade an appealing immune-modulating therapeutic target. We evaluated the correlation between CSF1R tumor RNA expression and outcome (pan-cancer setting). RNA expression was ranked as a percentile (0-100) using a standardized internal reference population (735 tumors; 35 histologies). Among 514 patients, there was no difference in survival from biopsy between high and low CSF1R expressors (< 50 percentile versus ≥ 50 percentile rank). There was also no significant difference in median progression-free or overall survival (from treatment) based on CSF1R expression in 21 patients who received CSF1R inhibitors (all p values ≥ 0.08). Concurrent upregulation of ≥ 2 additional immune checkpoint markers (e.g. PD-L1, BTLA, CTLA4, LAG3, TIM3) was observed in all tumor samples with CSF1R expression ≥ 50th percentile. Pending further large prospective studies, patients with high tumor CSF1R expression may need treatment that co-targets the specific immune checkpoint pathways activated in order to impact outcome.

T-cell priming transcriptomic markers: implications of immunome heterogeneity for precision immunotherapy.

Immune checkpoint blockade is effective for only a subset of cancers. Targeting T-cell priming markers (TPMs) may enhance activity, but proper application of these agents in the clinic is challenging due to immune complexity and heterogeneity. We interrogated transcriptomics of 15 TPMs (CD137, CD27, CD28, CD80, CD86, CD40, CD40LG, GITR, ICOS, ICOSLG, OX40, OX40LG, GZMB, IFNG, and TBX21) in a pan-cancer cohort (N = 514 patients, 30 types of cancer). TPM expression was analyzed for correlation with histological type, microsatellite instability high (MSI-H), tumor mutational burden (TMB), and programmed death-ligand 1 (PD-L1) expression. Among 514 patients, the most common histological types were colorectal (27%), pancreatic (11%), and breast cancer (10%). No statistically significant association between histological type and TPM expression was seen. In contrast, expression of GZMB (granzyme B, a serine protease stored in activated T and NK cells that induces cancer cell apoptosis) and IFNG (activates cytotoxic T cells) were significantly higher in tumors with MSI-H, TMB ≥ 10 mutations/mb and PD-L1 ≥ 1%. PD-L1 ≥ 1% was also associated with significantly higher CD137, GITR, and ICOS expression. Patients' tumors were classified into "Hot", "Mixed", or "Cold" clusters based on TPM expression using hierarchical clustering. The cold cluster showed a significantly lower proportion of tumors with PD-L1 ≥ 1%. Overall, 502 patients (98%) had individually distinct patterns of TPM expression. Diverse expression patterns of TPMs independent of histological type but correlating with other immunotherapy biomarkers (PD-L1 ≥ 1%, MSI-H and TMB ≥ 10 mutations/mb) were observed. Individualized selection of patients based on TPM immunomic profiles may potentially help with immunotherapy optimization.

NMFClustering: Accessible NMF-based clustering utilizing GPU acceleration.

Non-negative Matrix Factorization (NME) is an algorithm that can reduce high dimensional datasets of tens of thousands of genes to a handful of metagenes which are biologically easier to interpret. Application of NMF on gene expression data has been limited by its computationally intensive nature, which hinders its use on large datasets such as single-cell RNA sequencing (scRNA-seq) count matrices. We have implemented NMF based clustering to run on high performance GPU compute nodes using Cupy, a GPU backed python library, and the Message Passing Interface (MPI). This reduces the computation time by up to three orders of magnitude and makes the NMF Clustering analysis of large RNA-Seq and scRNA-seq datasets practical. We have made the method freely available through the GenePatten gateway, which provides free public access to hundreds of tools for the analysis and visualization of multiple 'omic data types. Its web-based interface gives easy access to these tools and allows the creation of multi-step analysis pipelnes on high performance computing (HPC) culsters that enable reproducible in silco research for non-programmers.

Dosing of 3 Targeted Agents in Novel Drug Combinations Used at the Precision Medicine Clinic of the University of California San Diego.

BACKGROUND: The diversity in the genomic landscape of advanced and metastatic tumors calls for combination therapies based on the genomic signature associated with each tumor. Determining safe and tolerable doses for novel combinations of oncology drugs is essential for a precision medicine approach, but can also require dose reductions. Trametinib, palbociclib, and everolimus are among the targeted therapies most often used in novel combinations at our precision medicine clinic. OBJECTIVE: To evaluate the safe, tolerable dosing of trametinib, palbociclib, and everolimus when used as part of novel combinations with other agents for the treatment of advanced or metastatic solid tumors. METHODS: This retrospective study included adult patients with advanced or metastatic solid tumors who received trametinib, everolimus, or palbociclib plus other therapies as a part of novel combinations between December 2011 and July 2018 at the University of California San Diego. Patients were excluded if they received trametinib, everolimus, or palbociclib in standard combinations, such as dabrafenib plus trametinib, everolimus plus fulvestrant, everolimus plus letrozole, and palbociclib plus letrozole. Dosing and adverse events were determined through a review of the electronic medical records. A safe, tolerable drug combination dose was defined as being tolerated for at least 1 month, with no clinically significant serious adverse events. RESULTS: A safe, tolerable dose was determined for 76% of the 71 patients who received trametinib, 88% of the 48 patients who received everolimus, and 73% of the 41 patients receiving palbociclib when used in combination with other therapies. For patients with clinically significant adverse events, dose reductions were attempted in 30% of the trametinib recipients, in 17% of everolimus recipients, and in 45% of palbociclib recipients. When used in combination with other therapies, the optimal dosing of trametinib, palbociclib, and everolimus was lower than the standard single-agent dosing: it was 1 mg daily for trametinib; 5 mg daily for everolimus; and 75 mg daily, for 3 weeks on and 1 week off for palbociclib. Of note, everolimus could not be given concomitantly with trametinib at these doses. CONCLUSION: Safe and tolerable dosing of novel combination therapies that includes trametinib, everolimus, or palbociclib is feasible for a precision medicine approach. However, neither results from this study nor results from previous studies could support the use of everolimus in combination with trametinib, even at reduced doses.

Personalized matched targeted therapy in advanced pancreatic cancer: a pilot cohort analysis.

Despite progress, 2-year pancreatic cancer survival remains dismal. We evaluated a biomarker-driven, combination/N-of-one strategy in 18 patients (advanced/metastatic pancreatic cancer) (from Molecular Tumor Board). Targeted agents administered/patient = 2.5 (median) (range, 1-4); first-line therapy (N = 5); second line, (N = 13). Comparing patients (high versus low degrees of matching) (matching score ≥50% versus <50%; reflecting number of alterations matched to targeted agents divided by number of pathogenic alterations), survival was significantly longer (hazard ratio [HR] 0.24 (95% confidence interval [CI], 0.078-0.76, P = 0.016); clinical benefit rates (CBR) (stable disease ≥6 months/partial/complete response) trended higher (45.5 vs 0.0%, P = 0.10); progression-free survival, HR, 95% CI, 0.36 (0.12-1.10) (p = 0.075). First versus ≥2nd-line therapy had higher CBRs (80.0 vs 7.7%, P = 0.008). No grade 3-4 toxicities occurred. The longest responder achieved partial remission (17.5 months) by co-targeting MEK and CDK4/6 alterations (chemotherapy-free). Therefore, genomically matched targeted agent combinations were active in these advanced pancreatic cancers. Larger prospective trials are warranted.

Current management of benign retroperitoneal tumors.

Benign retroperitoneal tumors (BRT) represent a rare group of heterogeneous diseases. The literature lacks high-quality evidence about the optimal management of BRT, and most of the information available takes the form of case reports or case series. The aim of this review is to provide an overview of current management strategies for adult patients with BRT. A literature search using PubMed indexed articles was conducted and BRT were classified into five different biological subgroups: 1) lipomatous tumors, 2) smooth muscle tumors, 3) peripheral nerve sheath tumors, 4) myofibroblastic tumors, and 5) others. Tumors that are primarily pelvic in origin were excluded. Despite the significant heterogeneity of the disease, several generic considerations have emerged and can be applied to the management of BRT. Specifically, the risk of misdiagnosing a BRT with another pathology such as retroperitoneal sarcoma is notable. When encountered, suspected BRT should therefore be referred to a specialized sarcoma center. Multidisciplinary tumor boards, present at these centers, have a pivotal role in managing BRT. The decision of whether to offer surgery, nonsurgical treatment or a "watch-and-wait" approach should be made after multidisciplinary discussion, depending on tumor histology. Moving forward, collaborative research efforts dedicated to BRT remain crucial in gathering evidence and knowledge to further optimize patient care.

NMF Clustering: Accessible NMF-based Clustering Utilizing GPU Acceleration.

Non-negative Matrix Factorization (NMF) is an algorithm that can reduce high dimensional datasets of tens of thousands of genes to a handful of metagenes which are biologically easier to interpret. Application of NMF on gene expression data has been limited by its computationally intensive nature, which hinders its use on large datasets such as single-cell RNA sequencing (scRNA-seq) count matrices. We have implemented NMF based clustering to run on high performance GPU compute nodes using CuPy, a GPU backed python library, and the Message Passing Interface (MPI). This reduces the computation time by up to three orders of magnitude and makes the NMF Clustering analysis of large RNA-Seq and scRNA-seq datasets practical. We have made the method freely available through the GenePattern gateway, which provides free public access to hundreds of tools for the analysis and visualization of multiple 'omic data types. Its web-based interface gives easy access to these tools and allows the creation of multi-step analysis pipelines on high performance computing (HPC) clusters that enable reproducible in silico research for non-programmers.

NCCN Guidelines® Insights: Gastrointestinal Stromal Tumors, Version 2.2022.

Gastrointestinal stromal tumors (GIST) are the most common type of soft tissue sarcoma that occur throughout the gastrointestinal tract. Most of these tumors are caused by oncogenic activating mutations in the KIT or PDGFRA genes. The NCCN Guidelines for GIST provide recommendations for the diagnosis, evaluation, treatment, and follow-up of patients with these tumors. These NCCN Guidelines Insights summarize the panel discussion behind recent important updates to the guidelines, including revised systemic therapy options for unresectable, progressive, or metastatic GIST based on mutational status, and updated recommendations for the management of GIST that develop resistance to specific tyrosine kinase inhibitors.

Anti-KIT DNA aptamer-conjugated porous silicon nanoparticles for the targeted detection of gastrointestinal stromal tumors.

Evaluation of Gastrointestinal Stromal Tumors (GIST) during initial clinical staging, surgical intervention, and postoperative management can be challenging. Current imaging modalities (e.g., PET and CT scans) lack sensitivity and specificity. Therefore, advanced clinical imaging modalities that can provide clinically relevant images with high resolution would improve diagnosis. KIT is a tyrosine kinase receptor overexpressed on GIST. Here, the application of a specific DNA aptamer targeting KIT, decorated onto a fluorescently labeled porous silicon nanoparticle (pSiNP), is used for the in vitro & in vivo imaging of GIST. This nanoparticle platform provides high-fidelity GIST imaging with minimal cellular toxicity. An in vitro analysis shows greater than 15-fold specific KIT protein targeting compared to the free KIT aptamer, while in vivo analyses of GIST-burdened mice that had been injected intravenously (IV) with aptamer-conjugated pSiNPs show extensive nanoparticle-to-tumor signal co-localization (>90% co-localization) compared to control particles. This provides an effective platform for which aptamer-conjugated pSiNP constructs can be used for the imaging of KIT-expressing cancers or for the targeted delivery of therapeutics.

Pan-cancer molecular tumor board experience with biomarker-driven precision immunotherapy.

Despite remarkable responses to immune checkpoint blockade (ICB) in some advanced cancers, most patients do not benefit, perhaps due to the complexity of tumor/immune/genome interactions. We implemented a multidisciplinary Molecular Tumor Board (MTB) that reviewed multi-omic cancer characteristics to develop N-of-One therapies for patients in the pan-cancer, advanced, refractory setting. This study evaluates the experience of 80 patients who were presented to the MTB and received a treatment regimen that included ICB. Overall, 60/80 patients (75%) who received ICB following MTB discussion had a high degree of matching between tumor molecular characteristics, including ICB biomarkers (reflected by a high Matching Score (≥50%)) and therapy administered. Patients with high versus low Matching Score experienced significantly longer median progression-free survival (6.4 vs. 3.0 months; p = 0.011) and median overall survival (15.3 vs. 4.7 months; p = 0.014) and higher clinical benefit rates (stable disease ≥6 months/partial response/complete response) (53% vs. 21%, p = 0.019). Although most patients (52/80 (65%)) received a personalized combination therapy (e.g., targeted, hormonal, chemotherapy, or a second immunotherapy agent), administering >1 drug was not associated with outcome. Only degree of matching and age, but no other variables, including individual biomarkers (e.g., microsatellite status, tumor mutational burden, or PD-L1 status), were independently correlated with outcome. In the pan-cancer setting, the MTB facilitated a precision medicine strategy to match therapeutic regimens that included ICB alone or combined with matched targeted drugs to patients with advanced malignancy, which was associated with improved clinical outcomes.